Distributions are considered any population that has a scattering of data. It’s important to determine the kind of distribution that population has so we can apply the correct statistical methods when analyzing it.

## Types of Data in a Distribution

Typically discreet or continuous

### Discrete Distributions

Discrete= counted

### Continuous Distributions

Continuous = can take many different values

#### Non-normal distributions

Also see Non-normal distributions

## Evaluating a Distribution

• Look for hard stops
• # of values
• Evaluate the shape
• Exponential – hockey stick
• Has a constant failure rate as it will always have the same shape parameters.
• Gamma
• Contains variable shape and scale parameters.
• Uniform – Flat bar, constant: used to test random # generators- everything has the same probability.
• Log-normal (lognormal)– Used in maintainability analysis – bringing broken tools back on line tends to follow lognormal.
• takes on different shapes depending on the mean and standard deviation.
• http://www.free-six-sigma.com/lognormal-distribution.html (Lognormal distribution)
• Bi-modal – 2 sources of data coming into a single process screen.
• Weibull
• Assumes many shapes depending upon the shape, scale, and location parameters.Effect of Shape parameter B on Weibull distribution:

## Symmetric Distribution

Both sides of the mean match & mirror each other.

## Asymmetric Distribution

Both sides of the mean do NOT match.

## Skewed Distribution

The data set has outliers. Skewness is a measure of the lack of symmetry.

When the outliers are high on one side, the mean will be > Median